Sweden as a footballing nation (men’s football) has not yet made a significant impact internationally, compared to the so-called Big Five (England, Germany, Spain, Italy and France). Historically, Sweden has not been known for mass producing world-class players. However, the tide may be turning. Today, Sweden might be in its best position recorded with standout players like Isak, Gyökeres, Kulusevski, all whom are chasing the legacy of Zlatan Ibrahimović.
Now, the question arises: who could possibly be Swedens next star striker? Amomg the candidates are two exciting forwards—Alexander Isak and Viktor Gyökeres—who both have made significant strides in their careers in recent years. This project aims explore this question purely based on recorded player metrics. Specifically, player metrics from Domestic Leagues and European Cups during the seasons 2023-2025.
By comparing the performance of Isak and Gyökeres across various metrics to determine their strengths and weaknesses. By focusing on key areas such as goal-scoring efficiency, creativity, and possession metrics, we aim to provide an objective evaluation of their contributions on the pitch.
The data used in this analysis was extracted from the website
https://fbref.com/en/, using the package
worldfootballR. For more coding details regarding the
collection of data, see data.ipynb in the repo.
The selection of players are based on:
The interactive barplots displayed have been created using the
package plotly. It includes a drop-down button menu, where
each button yields a different plot. Furthermore, you can disable a
displayed metric by clicking on the specific metric found in the
legend.
Viktor Gyökeres and Alexander Isak demonstrate exceptional attacking efficiency among the analyzed strikers. Ranking 2nd and 4th (0.2 percent behind 3rd in Kane) in Goal Conversion Rate (Goals /Shots), Gyökeres \((28.4\%)\) and Isak \((26.3\%)\) showcase their clinical finishing abilites. They also maintain an impressive Shooting Efficiency, with almost half of the shots recorded being on target. The recorded metrics are similar to the likes of Robert Lewandowski and Harry Kane, who both are proven star players.
When comparing Expected Goals per 90 minutes (xG/90) to Actual Goals per 90 minutes (Goals/90), Gyökeres significantly exceeds expectations, highlighting his ability to convert opportunities into goals at an exceptional rate. Conversely, Isak performs consistently with his expected values, neither underperforming nor overperforming. Interestingly, elite strikers like Kylian Mbappé and Erling Haaland underperform their average xG/90, reflecting potential inefficiencies or tactical dependencies.
Analyzing Goal Creating Actions per 90 minutes (GCA/90) and Shot Creating Actions per 90 minutes (SCA/90), Gyökeres consistently outperforms Isak. Furthermore, Gyökeres excels in Assists per 90 minutes, surpassing Isak in creating scoring opportunities for teammates. Notably, both players shows slight underperformance in the Assists per 90 metric, with only three strikers outperforming their respective expected metric. Despite this, Gyökeres’ higher involvement in both GCA/90 and SCA/90 highlights his integral role in driving his team’s offensive output.
Gyökeres’ overall attacking metrics are exceptionally strong, compared to the other players in the dataset. His ability to consistently create and assist opportunities, and score goals, places him among the most dynamic forwards in the dataset, with similar recorded metrics to Harry Kane. On the other hand, Isak demonstrates a slightly more balanced profile, with solid Creating Action metrics but clinically exceptional.
Viktor Gyökeres and Alexander Isak demonstrate strong possession metrics relative to other strikers. In particular, Gyökeres excels in Progressive Carries per 90, Final Third Carries per 90 as well as Progressive Passes Received per 90, indicating his ability to advance the ball effectively both on- and off the ball. This highligts his importance in transitions and build-up play. Conversley, Isak scores lower in these three metrics but performs better in Progressive Passes per, reflecting a more balanced role in in linking play and distrbuting the ball. Isak also shows above-average performance in Successful Take-Ons (%), while Gyökeres ranks second to last in this metric. This suggests that Isak has an edge in dribbling proficiency and retaining possession under pressure. However, Gyökeres and Isak ranks 1st and 2nd in Dispossessed per 90 (Dis/90), which may reflect their willingness to take more risks in possession compared to the other strikers.
Gyökeres’ possession metrics emphasize his versatility and pivotal role in his team’s attacking structure. On the other hand, Isak’s metrics highlights a different skill set, with potentially a more technical and creative approach to ball progression. Together, these stats displays a nuanced perspective of their respective playing style but should be part of a broader analysis incorporating team and tactical context.
The final visualization is a side by side comparison using a radar
chart, created using the package fmsb.
The radar chart consists of some importants metrics considered earlier; Goals per 90, Assists per 90, SoT% (Proportions of shots being on target), Goal Conversion% (Proportions of shots ending up as Goal), GCA/90, SCA/90, Succ% (Successful Take-Ons) and Prg/90 (Average of total progression metrics per 90). Each metrics upper bound is based on the datasets maximum value (e.g Gyökeres has the maximum recorded SCA90).
Overall, the radar chart gives a quick summary of the two players strenghts and weaknesses relative to the other players in the data.
Based on the data at hand, Gyökeres is the better all around striker outperforming Isak in almost all metrics. Gyökeres is a proficient striker and competes with the worlds best strikers to date.
Nontheless, it is important to highlight that these results should be interpreted cautiously. There are several non-measurable factors that can impact the overall data, for example: differences in league strenght, team quality, and tactical systems might influence these metrics.